A Cognitive Assistant for Entry, Descent, and Landing Architecture Analysis

Entry, Descent and Landing (EDL) architecture performance and uncertainty analysis relies heavily on end-to-end simulation given that EDL system verification and validation is limited in Earth environments. Overall system assessment and success criteria evaluation are performed by employing Monte Carlo dispersion analysis. These simulations produce large data sets that are manually analyzed by the subject matter experts, trying to identify correlations between parameters and assessing sensitivity of figures of merit to simulation parameters. Such analysis work is critical, given that it could lead, for example, to the discovery of major flaws in a design. While the subject matter experts can leverage their knowledge and expertise with past systems to identify issues and features of interest in the current dataset, the next generation of EDL systems will make use of new technologies to address the issue of landing larger payloads, and may present unprecedented challenges that may be missed by the human. In this paper, we suggest integrating Daphne, a cognitive assistant, into the process of EDL architecture analysis to support EDL experts by identifying key factors that impact EDL system metrics. Specifically, this paper describes the current capabilities of Daphne as a platform for EDL architecture analysis by means of a case study of a sample EDL architecture for an ongoing NASA mission, Mars 2020. Given that the work presented in this paper is in its early development, the paper focuses on the description of the expert knowledge base and historical database developed for the cognitive assistant, as well as on describing how experts can use it to obtain information relevant to their EDL analysis process by means of natural language or web visual interactions, thus reducing the effort of searching for relevant information from multiple sources.